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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">src/caffe/util</a> - im2col.cpp<span style="font-size: 80%;"> (source / <a href="im2col.cpp.func-sort-c.html">functions</a>)</span></td>
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            <td width="10%" class="headerCovTableHead">Hit</td>
            <td width="10%" class="headerCovTableHead">Total</td>
            <td width="15%" class="headerCovTableHead">Coverage</td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">19</td>
            <td class="headerCovTableEntry">87</td>
            <td class="headerCovTableEntryLo">21.8 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:25:26</td>
            <td></td>
            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">11</td>
            <td class="headerCovTableEntryLo">18.2 %</td>
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            <td class="headerItem">Legend:</td>
            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #include &lt;vector&gt;</a>
<span class="lineNum">       2 </span>            : 
<span class="lineNum">       3 </span>            : #include &quot;caffe/util/im2col.hpp&quot;
<span class="lineNum">       4 </span>            : #include &quot;caffe/util/math_functions.hpp&quot;
<span class="lineNum">       5 </span>            : 
<span class="lineNum">       6 </span>            : namespace caffe {
<span class="lineNum">       7 </span>            : 
<span class="lineNum">       8 </span>            : // Function uses casting from int to unsigned to compare if value of
<span class="lineNum">       9 </span>            : // parameter a is greater or equal to zero and lower than value of
<span class="lineNum">      10 </span>            : // parameter b. The b parameter is of type signed and is always positive,
<span class="lineNum">      11 </span>            : // therefore its value is always lower than 0x800... where casting
<span class="lineNum">      12 </span>            : // negative value of a parameter converts it to value higher than 0x800...
<span class="lineNum">      13 </span>            : // The casting allows to use one condition instead of two.
<span class="lineNum">      14 </span>            : inline bool is_a_ge_zero_and_a_lt_b(int a, int b) {
<span class="lineNum">      15 </span><span class="lineCov">  510000000 :   return static_cast&lt;unsigned&gt;(a) &lt; static_cast&lt;unsigned&gt;(b);</span>
<span class="lineNum">      16 </span>            : }
<a name="17"><span class="lineNum">      17 </span>            : </a>
<span class="lineNum">      18 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      19 </span><span class="lineCov">      20000 : void im2col_cpu(const Dtype* data_im, const int channels,</span>
<span class="lineNum">      20 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">      21 </span>            :     const int pad_h, const int pad_w,
<span class="lineNum">      22 </span>            :     const int stride_h, const int stride_w,
<span class="lineNum">      23 </span>            :     const int dilation_h, const int dilation_w,
<span class="lineNum">      24 </span>            :     Dtype* data_col) {
<span class="lineNum">      25 </span>            :   const int output_h = (height + 2 * pad_h -
<span class="lineNum">      26 </span><span class="lineCov">      20000 :     (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;</span>
<span class="lineNum">      27 </span>            :   const int output_w = (width + 2 * pad_w -
<span class="lineNum">      28 </span><span class="lineCov">      20000 :     (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;</span>
<span class="lineNum">      29 </span><span class="lineCov">      20000 :   const int channel_size = height * width;</span>
<span class="lineNum">      30 </span><span class="lineCov">     440000 :   for (int channel = channels; channel--; data_im += channel_size) {</span>
<span class="lineNum">      31 </span><span class="lineCov">    2310000 :     for (int kernel_row = 0; kernel_row &lt; kernel_h; kernel_row++) {</span>
<span class="lineNum">      32 </span><span class="lineCov">   11550000 :       for (int kernel_col = 0; kernel_col &lt; kernel_w; kernel_col++) {</span>
<span class="lineNum">      33 </span><span class="lineCov">    5250000 :         int input_row = -pad_h + kernel_row * dilation_h;</span>
<span class="lineNum">      34 </span><span class="lineCov">   97250000 :         for (int output_rows = output_h; output_rows; output_rows--) {</span>
<span class="lineNum">      35 </span><span class="lineCov">   46000000 :           if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {</span>
<span class="lineNum">      36 </span><span class="lineNoCov">          0 :             for (int output_cols = output_w; output_cols; output_cols--) {</span>
<span class="lineNum">      37 </span><span class="lineNoCov">          0 :               *(data_col++) = 0;</span>
<span class="lineNum">      38 </span>            :             }
<span class="lineNum">      39 </span>            :           } else {
<span class="lineNum">      40 </span><span class="lineCov">   46000000 :             int input_col = -pad_w + kernel_col * dilation_w;</span>
<span class="lineNum">      41 </span><span class="lineCov">  974000000 :             for (int output_col = output_w; output_col; output_col--) {</span>
<span class="lineNum">      42 </span><span class="lineCov">  464000000 :               if (is_a_ge_zero_and_a_lt_b(input_col, width)) {</span>
<span class="lineNum">      43 </span><span class="lineCov">  464000000 :                 *(data_col++) = data_im[input_row * width + input_col];</span>
<span class="lineNum">      44 </span>            :               } else {
<span class="lineNum">      45 </span><span class="lineNoCov">          0 :                 *(data_col++) = 0;</span>
<span class="lineNum">      46 </span>            :               }
<span class="lineNum">      47 </span><span class="lineCov">  464000000 :               input_col += stride_w;</span>
<span class="lineNum">      48 </span>            :             }
<span class="lineNum">      49 </span>            :           }
<span class="lineNum">      50 </span><span class="lineCov">   46000000 :           input_row += stride_h;</span>
<span class="lineNum">      51 </span>            :         }
<span class="lineNum">      52 </span>            :       }
<span class="lineNum">      53 </span>            :     }
<span class="lineNum">      54 </span>            :   }
<span class="lineNum">      55 </span><span class="lineCov">      20000 : }</span>
<span class="lineNum">      56 </span>            : 
<span class="lineNum">      57 </span>            : // Explicit instantiation
<span class="lineNum">      58 </span>            : template void im2col_cpu&lt;float&gt;(const float* data_im, const int channels,
<span class="lineNum">      59 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">      60 </span>            :     const int pad_h, const int pad_w, const int stride_h,
<span class="lineNum">      61 </span>            :     const int stride_w, const int dilation_h, const int dilation_w,
<span class="lineNum">      62 </span>            :     float* data_col);
<span class="lineNum">      63 </span>            : template void im2col_cpu&lt;double&gt;(const double* data_im, const int channels,
<span class="lineNum">      64 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">      65 </span>            :     const int pad_h, const int pad_w, const int stride_h,
<span class="lineNum">      66 </span>            :     const int stride_w, const int dilation_h, const int dilation_w,
<span class="lineNum">      67 </span>            :     double* data_col);
<a name="68"><span class="lineNum">      68 </span>            : </a>
<span class="lineNum">      69 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      70 </span><span class="lineNoCov">          0 : inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,</span>
<span class="lineNum">      71 </span>            :     const int num_spatial_axes, const int* im_shape, const int* col_shape,
<span class="lineNum">      72 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">      73 </span>            :     const int* dilation, Dtype* data_output) {
<span class="lineNum">      74 </span><span class="lineNoCov">          0 :   if (!im2col) {</span>
<span class="lineNum">      75 </span><span class="lineNoCov">          0 :     int im_size = im_shape[0];</span>
<span class="lineNum">      76 </span><span class="lineNoCov">          0 :     for (int i = 0; i &lt; num_spatial_axes; ++i) {</span>
<span class="lineNum">      77 </span><span class="lineNoCov">          0 :       im_size *= im_shape[1 + i];</span>
<span class="lineNum">      78 </span>            :     }
<span class="lineNum">      79 </span><span class="lineNoCov">          0 :     caffe_set(im_size, Dtype(0), data_output);</span>
<span class="lineNum">      80 </span>            :   }
<span class="lineNum">      81 </span>            :   int kernel_size = 1;
<span class="lineNum">      82 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; num_spatial_axes; ++i) {</span>
<span class="lineNum">      83 </span><span class="lineNoCov">          0 :     kernel_size *= kernel_shape[i];</span>
<span class="lineNum">      84 </span>            :   }
<span class="lineNum">      85 </span><span class="lineNoCov">          0 :   const int channels_col = col_shape[0];</span>
<span class="lineNum">      86 </span><span class="lineNoCov">          0 :   vector&lt;int&gt; d_offset(num_spatial_axes, 0);</span>
<span class="lineNum">      87 </span><span class="lineNoCov">          0 :   vector&lt;int&gt; d_iter(num_spatial_axes, 0);</span>
<span class="lineNum">      88 </span><span class="lineNoCov">          0 :   for (int c_col = 0; c_col &lt; channels_col; ++c_col) {</span>
<span class="lineNum">      89 </span>            :     // Loop over spatial axes in reverse order to compute a per-axis offset.
<span class="lineNum">      90 </span>            :     int offset = c_col;
<span class="lineNum">      91 </span><span class="lineNoCov">          0 :     for (int d_i = num_spatial_axes - 1; d_i &gt;= 0; --d_i) {</span>
<span class="lineNum">      92 </span><span class="lineNoCov">          0 :       if (d_i &lt; num_spatial_axes - 1) {</span>
<span class="lineNum">      93 </span><span class="lineNoCov">          0 :         offset /= kernel_shape[d_i + 1];</span>
<span class="lineNum">      94 </span>            :       }
<span class="lineNum">      95 </span><span class="lineNoCov">          0 :       d_offset[d_i] = offset % kernel_shape[d_i];</span>
<span class="lineNum">      96 </span>            :     }
<span class="lineNum">      97 </span><span class="lineNoCov">          0 :     for (bool incremented = true; incremented; ) {</span>
<span class="lineNum">      98 </span>            :       // Loop over spatial axes in forward order to compute the indices in the
<span class="lineNum">      99 </span>            :       // image and column, and whether the index lies in the padding.
<span class="lineNum">     100 </span>            :       int index_col = c_col;
<span class="lineNum">     101 </span><span class="lineNoCov">          0 :       int index_im = c_col / kernel_size;</span>
<span class="lineNum">     102 </span>            :       bool is_padding = false;
<span class="lineNum">     103 </span><span class="lineNoCov">          0 :       for (int d_i = 0; d_i &lt; num_spatial_axes; ++d_i) {</span>
<span class="lineNum">     104 </span><span class="lineNoCov">          0 :         const int d = d_iter[d_i];</span>
<span class="lineNum">     105 </span><span class="lineNoCov">          0 :         const int d_im = d * stride[d_i] - pad[d_i] +</span>
<span class="lineNum">     106 </span><span class="lineNoCov">          0 :             d_offset[d_i] * dilation[d_i];</span>
<span class="lineNum">     107 </span><span class="lineNoCov">          0 :         is_padding |= d_im &lt; 0 || d_im &gt;= im_shape[d_i + 1];</span>
<span class="lineNum">     108 </span><span class="lineNoCov">          0 :         index_col *= col_shape[d_i + 1];</span>
<span class="lineNum">     109 </span><span class="lineNoCov">          0 :         index_col += d;</span>
<span class="lineNum">     110 </span><span class="lineNoCov">          0 :         index_im *= im_shape[d_i + 1];</span>
<span class="lineNum">     111 </span><span class="lineNoCov">          0 :         index_im += d_im;</span>
<span class="lineNum">     112 </span>            :       }
<span class="lineNum">     113 </span><span class="lineNoCov">          0 :       if (im2col) {</span>
<span class="lineNum">     114 </span><span class="lineNoCov">          0 :         if (is_padding) {</span>
<span class="lineNum">     115 </span><span class="lineNoCov">          0 :           data_output[index_col] = 0;</span>
<span class="lineNum">     116 </span>            :         } else {
<span class="lineNum">     117 </span><span class="lineNoCov">          0 :           data_output[index_col] = data_input[index_im];</span>
<span class="lineNum">     118 </span>            :         }
<span class="lineNum">     119 </span><span class="lineNoCov">          0 :       } else if (!is_padding) {  // col2im</span>
<span class="lineNum">     120 </span><span class="lineNoCov">          0 :         data_output[index_im] += data_input[index_col];</span>
<span class="lineNum">     121 </span>            :       }
<span class="lineNum">     122 </span>            :       // Loop over spatial axes in reverse order to choose an index,
<span class="lineNum">     123 </span>            :       // like counting.
<span class="lineNum">     124 </span>            :       incremented = false;
<span class="lineNum">     125 </span><span class="lineNoCov">          0 :       for (int d_i = num_spatial_axes - 1; d_i &gt;= 0; --d_i) {</span>
<span class="lineNum">     126 </span><span class="lineNoCov">          0 :         const int d_max = col_shape[d_i + 1];</span>
<span class="lineNum">     127 </span>            :         DCHECK_LT(d_iter[d_i], d_max);
<span class="lineNum">     128 </span><span class="lineNoCov">          0 :         if (d_iter[d_i] == d_max - 1) {</span>
<span class="lineNum">     129 </span><span class="lineNoCov">          0 :           d_iter[d_i] = 0;</span>
<span class="lineNum">     130 </span>            :         } else {  // d_iter[d_i] &lt; d_max - 1
<span class="lineNum">     131 </span><span class="lineNoCov">          0 :           ++d_iter[d_i];</span>
<span class="lineNum">     132 </span>            :           incremented = true;
<span class="lineNum">     133 </span><span class="lineNoCov">          0 :           break;</span>
<span class="lineNum">     134 </span>            :         }
<span class="lineNum">     135 </span>            :       }
<span class="lineNum">     136 </span>            :     }  // while(incremented) {
<span class="lineNum">     137 </span>            :   }  // for (int c = 0; c &lt; channels_col; ++c) {
<span class="lineNum">     138 </span><span class="lineNoCov">          0 : }</span>
<a name="139"><span class="lineNum">     139 </span>            : </a>
<span class="lineNum">     140 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">     141 </span><span class="lineNoCov">          0 : void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,</span>
<span class="lineNum">     142 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     143 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     144 </span>            :     const int* dilation, Dtype* data_col) {
<span class="lineNum">     145 </span>            :   const bool kIm2Col = true;
<span class="lineNum">     146 </span><span class="lineNoCov">          0 :   im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape,</span>
<span class="lineNum">     147 </span>            :                   kernel_shape, pad, stride, dilation, data_col);
<span class="lineNum">     148 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">     149 </span>            : 
<span class="lineNum">     150 </span>            : // Explicit instantiation
<span class="lineNum">     151 </span>            : template void im2col_nd_cpu&lt;float&gt;(const float* data_im,
<span class="lineNum">     152 </span>            :     const int num_spatial_axes,
<span class="lineNum">     153 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     154 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     155 </span>            :     const int* dilation, float* data_col);
<span class="lineNum">     156 </span>            : template void im2col_nd_cpu&lt;double&gt;(const double* data_im,
<span class="lineNum">     157 </span>            :     const int num_spatial_axes,
<span class="lineNum">     158 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     159 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     160 </span>            :     const int* dilation, double* data_col);
<a name="161"><span class="lineNum">     161 </span>            : </a>
<span class="lineNum">     162 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">     163 </span><span class="lineNoCov">          0 : void col2im_cpu(const Dtype* data_col, const int channels,</span>
<span class="lineNum">     164 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">     165 </span>            :     const int pad_h, const int pad_w,
<span class="lineNum">     166 </span>            :     const int stride_h, const int stride_w,
<span class="lineNum">     167 </span>            :     const int dilation_h, const int dilation_w,
<span class="lineNum">     168 </span>            :     Dtype* data_im) {
<span class="lineNum">     169 </span><span class="lineNoCov">          0 :   caffe_set(height * width * channels, Dtype(0), data_im);</span>
<span class="lineNum">     170 </span>            :   const int output_h = (height + 2 * pad_h -
<span class="lineNum">     171 </span><span class="lineNoCov">          0 :     (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;</span>
<span class="lineNum">     172 </span>            :   const int output_w = (width + 2 * pad_w -
<span class="lineNum">     173 </span><span class="lineNoCov">          0 :     (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;</span>
<span class="lineNum">     174 </span>            :   const int channel_size = height * width;
<span class="lineNum">     175 </span><span class="lineNoCov">          0 :   for (int channel = channels; channel--; data_im += channel_size) {</span>
<span class="lineNum">     176 </span><span class="lineNoCov">          0 :     for (int kernel_row = 0; kernel_row &lt; kernel_h; kernel_row++) {</span>
<span class="lineNum">     177 </span><span class="lineNoCov">          0 :       for (int kernel_col = 0; kernel_col &lt; kernel_w; kernel_col++) {</span>
<span class="lineNum">     178 </span><span class="lineNoCov">          0 :         int input_row = -pad_h + kernel_row * dilation_h;</span>
<span class="lineNum">     179 </span><span class="lineNoCov">          0 :         for (int output_rows = output_h; output_rows; output_rows--) {</span>
<span class="lineNum">     180 </span><span class="lineNoCov">          0 :           if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {</span>
<span class="lineNum">     181 </span><span class="lineNoCov">          0 :             data_col += output_w;</span>
<span class="lineNum">     182 </span>            :           } else {
<span class="lineNum">     183 </span><span class="lineNoCov">          0 :             int input_col = -pad_w + kernel_col * dilation_w;</span>
<span class="lineNum">     184 </span><span class="lineNoCov">          0 :             for (int output_col = output_w; output_col; output_col--) {</span>
<span class="lineNum">     185 </span><span class="lineNoCov">          0 :               if (is_a_ge_zero_and_a_lt_b(input_col, width)) {</span>
<span class="lineNum">     186 </span><span class="lineNoCov">          0 :                 data_im[input_row * width + input_col] += *data_col;</span>
<span class="lineNum">     187 </span>            :               }
<span class="lineNum">     188 </span><span class="lineNoCov">          0 :               data_col++;</span>
<span class="lineNum">     189 </span><span class="lineNoCov">          0 :               input_col += stride_w;</span>
<span class="lineNum">     190 </span>            :             }
<span class="lineNum">     191 </span>            :           }
<span class="lineNum">     192 </span><span class="lineNoCov">          0 :           input_row += stride_h;</span>
<span class="lineNum">     193 </span>            :         }
<span class="lineNum">     194 </span>            :       }
<span class="lineNum">     195 </span>            :     }
<span class="lineNum">     196 </span>            :   }
<span class="lineNum">     197 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">     198 </span>            : 
<span class="lineNum">     199 </span>            : // Explicit instantiation
<span class="lineNum">     200 </span>            : template void col2im_cpu&lt;float&gt;(const float* data_col, const int channels,
<span class="lineNum">     201 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">     202 </span>            :     const int pad_h, const int pad_w, const int stride_h,
<span class="lineNum">     203 </span>            :     const int stride_w, const int dilation_h, const int dilation_w,
<span class="lineNum">     204 </span>            :     float* data_im);
<span class="lineNum">     205 </span>            : template void col2im_cpu&lt;double&gt;(const double* data_col, const int channels,
<span class="lineNum">     206 </span>            :     const int height, const int width, const int kernel_h, const int kernel_w,
<span class="lineNum">     207 </span>            :     const int pad_h, const int pad_w, const int stride_h,
<span class="lineNum">     208 </span>            :     const int stride_w, const int dilation_h, const int dilation_w,
<span class="lineNum">     209 </span>            :     double* data_im);
<a name="210"><span class="lineNum">     210 </span>            : </a>
<span class="lineNum">     211 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">     212 </span><span class="lineNoCov">          0 : void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,</span>
<span class="lineNum">     213 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     214 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     215 </span>            :     const int* dilation, Dtype* data_im) {
<span class="lineNum">     216 </span>            :   const bool kIm2Col = false;
<span class="lineNum">     217 </span><span class="lineNoCov">          0 :   im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape,</span>
<span class="lineNum">     218 </span>            :                      kernel_shape, pad, stride, dilation, data_im);
<span class="lineNum">     219 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">     220 </span>            : 
<span class="lineNum">     221 </span>            : // Explicit instantiation
<span class="lineNum">     222 </span>            : template void col2im_nd_cpu&lt;float&gt;(const float* data_col,
<span class="lineNum">     223 </span>            :     const int num_spatial_axes,
<span class="lineNum">     224 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     225 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     226 </span>            :     const int* dilation, float* data_im);
<span class="lineNum">     227 </span>            : template void col2im_nd_cpu&lt;double&gt;(const double* data_col,
<span class="lineNum">     228 </span>            :     const int num_spatial_axes,
<span class="lineNum">     229 </span>            :     const int* im_shape, const int* col_shape,
<span class="lineNum">     230 </span>            :     const int* kernel_shape, const int* pad, const int* stride,
<span class="lineNum">     231 </span>            :     const int* dilation, double* data_im);
<a name="232"><span class="lineNum">     232 </span>            : </a>
<span class="lineNum">     233 </span>            : 
<span class="lineNum">     234 </span><span class="lineCov">          2 : }  // namespace caffe</span>
</pre>
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